- All Journals
- Al-Qadisiyah University
- Basrah University
- Kirkuk University
- Mosul University
- The Iraqi Borad for Medical Specialization
- University of Anbar
- University of Technology
- Al Mustafa University
- Al-Zahraa University for Women
- Alnoor University
- Kunooz University
- Misan University
- Ninevah University
- Tikrit University
- University of Al-Hamdaniya
- University of Kerbala
Deep CNN Based Skin Lesion Image Denoising and Segmentation using Active Contour Method | ||
| Engineering and Technology Journal | ||
| Article 3, Volume 37, 11A, 2019, Pages 464-469 PDF (690.23 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.30684/etj.37.11A.3 | ||
| Authors | ||
| Hadeel N. Abdullah; Hala K. Abduljaleel | ||
| Electrical Engineering Department, University of Technology - Iraq | ||
| Abstract | ||
| Automatic skin lesion segmentation on skin images is an essential component in diagnosing skin cancer. Image de-noising in skin cancer lesion is a description of processing image which refers to image restoration techniques to develop an image in predefined touch. Then de-noising is the crucial step of image processing to restore the right quality image after that which can use in many processes like segmentation, detection. This work proposes a new technique for skin lesion tumor denoising and segmentation. Initially, using Deep Convolution Neural Network (CNN) to eliminate noise and undesired structures for the images. Then, a new mechanism is proposed to segment the skin lesion into skin images based on active_contour straight with morphological processes. Different noise removal and segmentation techniques on skin lesion images are applying and comparing. The proposed algorithm shows improvement in the results of both noise reduction and segmentation | ||
| Keywords | ||
| Convolutional Neural Networks; Skin Cancer; Denoising; Segmentation | ||
| References | ||
|
[1] A.H.H. Alasadi, and B.M. Alsafy, ―Diagnosis of Malignant Melanoma of Skin Cancer Types,‖ International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 4, No.5, pp.44-49, 2017. [2] A. Victor, and M. Ghalib, ―Automatic detection and classification of skin cancer,‖ International Journal of Intelligent Engineering and Systems, vol. 10, no. 3, pp.444-451, 2017. [3] M.A. Sheha, M.S. Mabrouk, and A.Sharawy, ―Automatic detection of melanoma skin cancer using texture analysis,” International Journal of Computer Applications, vol. 42, no. 20, pp.22-26, March 2012. [4] H.N. Abdullah, B.H. Abd, and S.H. Muhi, ―HighResolution Systems for Automated Diagnosis of Hepatitis,‖ In 2018 Third Scientific Conference of Electrical Engineering (SCEE), pp. 39-44, April 2019, IEEE. [5] D. Sharma, and S. Srivastava, ―Automatically detection of skin cancer by classification of neural network,‖ International Journal of Engineering and Technical Research, Vol. 4, Issue 1, pp.15-18, 2016. [6] H.N.Abdullah, J.S. Aziz, and A.N. Mohammed, ―Complex discrete wavelet transform-based image denoising,‖ Engineering and Technology Journal, vol. 29, no. 5, pp.833-850, 2011. [7] H.T. Lau, and A. Al-Jumaily, ―Automatically early detection of skin cancer: Study based on neural network classification,‖ In 2009 International Conference of Soft Computing and Pattern Recognition, pp. 375-380, December 2009, IEEE. [8] H.R. Mhaske, and D.A. Phalke, ―Melanoma skin cancer detection and classification based on supervised and unsupervised learning,‖ In 2013 International Conference on Circuits, Controls and Communications (CCUBE), pp. 1-5, December 2013, IEEE. [9] H.N. Abdullah, and M.A. Habtr, ―Brain Tumor Extraction Approach in MRI Images Based on Soft Computing Techniques,‖ In 2015 8th International Conference on Intelligent Networks and Intelligent Systems (ICINIS), pp. 21-24, November 2015, IEEE. [10] M.K.A. Mahmoud, A. Al-Jumaily, and M. Takruri, ―The automatic identification of melanoma by wavelet and curvelet analysis: a study based on neural network classification,‖ In 2011 11th International Conference on Hybrid Intelligent Systems (HIS), pp. 680-685, December 2011, IEEE. [11] J. Bai, Y. Sun, T. Fan, S. Song, and X. Zhang, Medical image denoising based on improving K-SVD and block-matching 3D filtering, In 2016 IEEE Region 10 Conference (TENCON), pp. 1624-1627, November 2016, IEEE. [12] F. Xu, J. G. Lu, and Y. X. Sun, ―Application of neural network in image processing,‖ Information and Control, vol. 32, no. 4, pp. 344– 351, 2003. [13] R. Garnavi, M. Aldeen, M.E. Celebi, A. Bhuiyan, C. Dolianitis, and G. Varigos, ―Automatic segmentation of dermoscopy images using histogram thresholding on optimal color channels, International Journal of Medicine and Medical Sciences, vol. 1, no. 2, pp.126-134,2010. [14] Y. Liu, ―Image denoising method based on a threshold, wavelet transform and genetic algorithm,‖ International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 8, no. 2, pp.29-40,2015. [15] S. Schulte, B. Huysmans, A. Pižurica, E.E. Kerre, and W. Philips, ―A new fuzzy-based wavelet shrinkage image denoising technique,‖ In International Conference on Advanced Concepts for Intelligent Vision Systems, pp. 12-23, Springer, Berlin, Heidelberg, September 2006. [16] K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, ―Beyond a gaussian denoiser: Residual learning of deep CNN for image denoising,‖ IEEE Transactions on Image Processing, vol. 26, no. 7, pp. 3142-3155,2017. [17] ―Addi project,‖ https://www.fc.up.pt/addi/ph2%20database.html | ||
|
Statistics Article View: 755 PDF Download: 486 |
||